GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. …”
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A data augmentation procedure to improve detection of spike ripples in brain voltage recordings by Emily D. Schlafly, Daniel Carbonero, Catherine J. Chu, Mark A. Kramer

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Study of the Application of Text Augmentation with Paraphrasing to Overcome Imbalanced Data in Indonesian Text Classification by Mutiara Indryan Sari, Lya Hulliyyatus Suadaa

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Doubly Structured Data Synthesis for Time-Series Energy-Use Data by Jiwoo Kim, Changhoon Lee, Jehoon Jeon, Jungwoong Choi, Joseph H. T. Kim

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Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: an in silico study by Alexander M. Zolotarev, Alexander M. Zolotarev, Kiane Johnson, Kiane Johnson, Yusuf Mohammad, Omnia Alwazzan, Omnia Alwazzan, Gregory Slabaugh, Gregory Slabaugh, Caroline H. Roney, Caroline H. Roney

“…While deep learning (DL) has shown promise in predicting ablation success, training such pipelines is limited by the availability of real patient data.MethodsIn this study, we generated synthetic fibrosis distributions using a denoising diffusion probabilistic model trained on a collection of 100 real LGE-MRI distributions. …”
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Enhancing voice spoofing detection in noisy environments using frequency feature masking augmentation by Soyul Han, Jaejin Seo, Sunmook Choi, Taein Kang, Sanghyeok Chung, Seungeun Lee, Seoyoung Park, Seungsang Oh, Il-Youp Kwak

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Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation by Oezdemir Cetin, Berkay Canel, Gamze Dogali, Unal Sakoglu

“…To address the issue of insufficient training data, data augmentation techniques have been implemented, enhancing the diversity and volume of the training set. …”
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Limited-Data Augmentation for Fault Diagnosis in Lithium-Ion Battery Energy Storage Systems via Transferable Conditional Diffusion by Zhipeng Yang, Yuhao Pan, Wenchao Liu, Jinhao Meng, Zhengxiang Song

“…This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. …”
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Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network for Aspect-Level Sentiment Analysis by Hongwei Tang, Haining Yan, Ran Song

“…To address these limitations, this paper presents a novel Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network (SSC-GRAN), a hybrid framework that synergistically integrates large language models (LLMs) with graph neural networks (GNNs). Our method introduces three key innovations: 1) a synthetic data augmentation paradigm leveraging LLMs to generate semantically coherent sentiment cues, thereby enriching aspect-opinion interactions; 2) a hierarchical graph architecture that models syntactic dependency structures and aspect-context relationships through relation-aware attention mechanisms; and 3) a contrastive learning objective that aligns representations from both authentic and synthetic data to enhance model robustness. …”
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Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions by Binrui Liu, Xinguang He, Wenkai Lyu, Lizhi Tao

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Posterior single implants immediately loaded using one abutment at one time and temporary abutment in the posterior mandible without bone augmentation: A report on six-month outcom... by Marković Jovana, Todorović Ana, Ilić Branislav, Marković Aleksa, Živanović Tanja, Veljković Kristina, Milinković Iva

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Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study by Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker

“…However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

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Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments by Faridoon Khan, Amena Urooj, Saud Ahmed Khan, Abdelaziz Alsubie, Zahra Almaspoor, Sara Muhammadullah

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Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning by Yulong Ji, Kunjin Zou, Bin Zou

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Role of ratio-based metrics in cardiology by A. O. Konradi, A. L. Maslyansky, E. P. Kolesova, E. V. Shlyakhto, P. L.M. Kerkhof

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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and... by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (&#x00B0;C), volume concentration (<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>), base fluid density (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {bf}}$ </tex-math></inline-formula>), density of primary and secondary nanoparticles (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np2}}$ </tex-math></inline-formula>), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. …”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. …”
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GNSS-Based Monitoring Methods for Mining Headframes by Xu Yang, Zhe Zhou, Yanzhao Yang, Xinxin Yao, Chao Liu, Lei Liu, Shicheng Xie

“…This study introduces an innovative GNSS-based monitoring system designed to evaluate deformation in mining headframes, effectively addressing the limitations of traditional methods, such as inadequate real-time capabilities and complex data processing requirements. …”
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Inference with Pólya-Gamma Augmentation for US Election Law by Adam C. Hall, Joseph Kang

“…Second, the approach can be successfully applied to several types of models, including nonlinear mixed-effects models for count data. The effectiveness of PG augmentation has led to its widespread adoption and implementation in statistical software packages, such as version 2.1 of the R package BayesLogit. …”
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